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Works278 from public data
- 658
- 538
- Beyond RNNs: Positional Self-Attention with Co-Attention for Video Question Answering304
This work proposes a new architecture, Positional Self-Attention with Coattention (PSAC), which does not require RNNs for video question answering and significantly outperforms the state-of-the-art on three tasks and attains comparable result on the Count task.
- Neighbourhood Watch: Referring Expression Comprehension via Language-Guided Graph Attention Networks290
A graph-based, language-guided attention mechanism that represents inter-object relationships, and properties with a flexibility and power impossible with competing approaches, and enables the comprehension decision to be visualizable and explainable.
- Self-Supervised Video Hashing With Hierarchical Binary Auto-Encoder282
This paper proposes a novel unsupervised video hashing framework dubbed SSVH, which is able to capture the temporal nature of videos in an end-to-end learning to hash fashion, and designs a hierarchical binary auto-encoder to model the temporal dependencies in videos with multiple granularities.
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- From Deterministic to Generative: Multimodal Stochastic RNNs for Video Captioning261
Experimental results on the challenging data sets, microsoft video description and microsoft research video-to-text, show that the proposed MS-RNN approach outperforms the state-of-the-art video captioning benchmarks.
- Hierarchical LSTMs with Adaptive Attention for Visual Captioning250
A hierarchical LSTM with adaptive attention (hLSTMat) approach for image and video captioning that utilizes the spatial or temporal attention for selecting specific regions or frames to predict the related words, while the adaptive attention is for deciding whether to depend on the visual information or the language context information.
- Deep adversarial metric learning for cross-modal retrieval217
A novel Deep Adversarial Metric Learning approach, termed DAML for cross-modal retrieval, which introduces a modality classifier to predict the modality of a transformed feature, which ensures that the transformed features are also statistically indistinguishable.
- Patch-Wise Attack for Fooling Deep Neural Network207
A patch-wise iterative algorithm -- a black-box attack towards mainstream normally trained and defense models, which differs from the existing attack methods manipulating pixel-wise noise, and can be generally integrated to any gradient-based attack methods.
- Binary Generative Adversarial Networks for Image Retrieval206
This paper uses binary generative adversarial networks (BGAN) to embed images to binary codes in an unsupervised way and significantly outperforms existing hashing methods by up to 107% in terms of mAP.
- Quantization-based hashing: a general framework for scalable image and video retrieval192
Quantization-based Hashing (QBH) is a generic framework which incorporates the advantages of quantization error reduction methods into conventional property preserving hashing methods and can be applied to both unsupervised and supervised hashing methods.
- Where Does It Exist: Spatio-Temporal Video Grounding for Multi-Form Sentences187
A novel Spatio-Temporal Graph Reasoning Network (STGRN) is proposed for this task, which builds a spatio-temporal region graph to capture the region relationships with temporal object dynamics, which involves the implicit and explicit spatial subgraphs in each frame and the temporal dynamic subgraph across frames.
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- Love Thy Neighbour: Automatic Animal Behavioural Classification of Acceleration Data Using the K-Nearest Neighbour Algorithm174
A method which allows researchers to classify accelerometer data into behavioural classes automatically using a primitive machine learning algorithm, k-nearest neighbour (KNN), and envisage that the KNN method may be coupled with methods for investigating animal position, such as GPS telemetry or dead-reckoning, in order to implement an integrated approach to movement ecology research.
- Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning171
The proposed framework utilizes the temporal attention for selecting specific frames to predict the related words, while the adjusted temporal attention is for deciding whether to depend on the visual information or the language context information.
- Template-Based Math Word Problem Solvers with Recursive Neural Networks168
This paper designs a recursive neural network to encode the quantity with Bi-LSTM and self attention, and infer the unknown operator nodes in a bottom-up manner, and establishes the superiority of the new framework as it improves the accuracy by a wide margin in two of the largest datasets.
- Matching User with Item Set: Collaborative Bundle Recommendation with Deep Attention Network148
This work contributes a neural network solution named DAM, short for Deep Attentive Multi-Task model, which is featured with two special designs: a factorized attention network to aggregate the item embeddings in a bundle to obtain the bundle’s representation and a multi-task manner to alleviate the scarcity of user-bundle interactions.
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- Part-Aware Transformer for Generalizable Person Re-identification131
A pure Transformer model is proposed (termed Part-aware Transformer) for DG-ReID by designing a proxy task, named Cross-ID Similarity Learning (CSL), to mine local visual information shared by different IDs, thus alleviating the side effect of domain-specific biases.
- Learning in high-dimensional multimedia data: the state of the art129
A survey of feature transformation, feature selection and feature encoding, three approaches fighting the consequences of the curse of dimensionality on multimedia learning.
- MathDQN: Solving Arithmetic Word Problems via Deep Reinforcement Learning125
This is the first attempt of applying deep reinforcement learning to solve arithmetic word problems and yields remarkable improvement on most of datasets and boosts the average precision among all the benchmark datasets by 15\%.
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- From General to Specific: Informative Scene Graph Generation via Balance Adjustment99
BA-SGG, a simple yet effective SGG framework based on balance adjustment but not the conventional distribution fitting, integrates two components: Semantic Adjustment (SA) and Balanced Predicate Learning (BPL) respectively for adjusting these imbalances.
- DePT: Decoupled Prompt Tuning92
The Decoupled Prompt Tuning (DePT) framework is proposed, which decouples base-specific knowledge from feature channels into an isolated feature space during prompt tuning, so as to maximally preserve task-shared knowl-edge in the original feature space for achieving better zero-shot generalization on new tasks.
- A Closer Look at Few-shot Classification Again92
This paper empirically prove that the training algorithm and the adaptation algorithm can be completely disentangled, which allows algorithm analysis and design to be done individually for each phase of few-shot classification.
- Unified Multivariate Gaussian Mixture for Efficient Neural Image Compression89
Extensive experiments on benchmark datasets against state-of-the-art datasets indicate the proposed novel vectorized prior has better rate-distortion performance and an impressive 3.18x compression speed up, giving the ability to perform real-time, high-quality variational image compression in practice.
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- Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks88
This work proposes a novel Natural Color Fool (NCF) which is guided by realistic color distributions sampled from a publicly available dataset and optimized by the authors' neighborhood search and initialization reset.
- Difficulty-Controllable Multi-hop Question Generation from Knowledge Graphs86
This work presents an end-to-end neural network-based method for automatic generation of complex multi-hop questions over knowledge graphs, and incorporates difficulty estimation based on named entity popularity, and makes use of this estimation to generate difficulty-controllable questions.
- Prototype-Based Embedding Network for Scene Graph Generation84
This paper claims that the predicate's category-inherent semantics can serve as class-wise prototypes in the semantic space for relieving the challenges and proposes the Prototype-based Embedding Network (PE-Net), which models entities/predicates with prototype-aligned compact and distinctive representations and thereby establishes matching between entity pairs and predicates in a common embedding space for relation recognition.
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- Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack80
This work proposes a parameter-free Adaptive Auto Attack (A3) evaluation method which addresses the efficiency and reliability in a test-time-training fashion and designs an Adaptive Direction Initialization strategy to speed up the evaluation.
- Common and distinct changes of default mode and salience network in schizophrenia and major depression80
Significant evidence is provided for distinct functional hyperconnectivity between DMN and SN in schizophrenia and major depression, while structural changes in DMn and SN were similar.
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- S2 Transformer for Image Captioning78
A Spatial- and Scale-aware Transformer (S2 Transformer) for image captioning, which resorts to feature clustering to help preserve spatial information for grid features and a simple weighted residual connection, named Scale-wise Reinforcement (SR) module, to simultaneously explore both low- and high-level encoded features with rich semantics.
- Dual Conditional GANs for Face Aging and Rejuvenation78
A novel dual conditional GAN mechanism is developed, which enables face aging and rejuvenation to be trained from multiple sets of unlabeled face images with different ages, and a loss function that accounts for the reconstruction error of images can preserve the personal identity.
- CoIN: A Benchmark of Continual Instruction tuNing for Multimodel Large Language Model77
A comprehensive benchmark is presented, namely Continual Instruction tuNing (CoIN), to assess existing MLLMs in the sequential instruction tuning paradigm and introduces MoELoRA to MLLMs which is effective to retain the previous instruction alignment.
- Structured Two-Stream Attention Network for Video Question Answering74
This paper proposes a Structured Two-stream Attention network, namely STA, to answer a free-form or open-ended natural language question about the content of a given video to infer rich longrange temporal structures in videos using the authors' structured segment component and encode text features.
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- MovieFactory: Automatic Movie Creation from Text using Large Generative Models for Language and Images72
MovieFactory, the first fully automated movie generation model to the best of the authors' knowledge, empowers users to create captivating movies with smooth transitions using simple text inputs, surpassing existing methods that produce soundless videos limited to a single scene of modest quality.
- Learnable Aggregating Net with Diversity Learning for Video Question Answering72
A novel architecture, namely Learnable Aggregating Net with Diversity learning (LAD-Net), for V-VQA, which automatically aggregates adaptively-weighted frame-level features to extract rich video (or question) context semantic information by imitating Bags-of-Words (BoW) quantization.
- Unified Binary Generative Adversarial Network for Image Retrieval and Compression68
A unified framework that restricts the input noise variable of generative adversarial networks to be binary and conditioned on the features of each input image, and simultaneously learns two binary representations per image: one for image retrieval and the other serving as image compression is proposed.
- From Pixels to Objects: Cubic Visual Attention for Visual Question Answering68
A Cubic Visual Attention (CVA) model is proposed by successfully applying a novel channel and spatial attention on object regions to improve VQA task and Experimental results show that the proposed method significantly outperforms the state-of-the-arts.
- Attention-based LSTM with Semantic Consistency for Videos Captioning61
This work proposes a novel end-to-end framework named aLSTMs, an attention-based LSTM model with semantic consistency, to transfer videos to natural sentences, and explores the correlation between multi-modal representations for generating sentences with rich semantic content.
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- Small Object Detection Using Deep Feature Pyramid Networks59
A new two-stage detector similar to Faster-RCNN is developed, and dense convolutional network is used to strengthen the feature transmission and multiplexing, which leads to more accurate classification with fewer parameters.
- Fused GRU with semantic-temporal attention for video captioning58
This work proposes an end-to-end pipeline named Fused GRU with Semantic-Temporal Attention (STA-FG), which can explicitly incorporate the high-level visual concepts to the generation of semantic-temporal attention for video captioning.
- Traffic sign detection and recognition based on pyramidal convolutional networks58
This work develops a two-stage network that achieves a very great performance and surpasses the other state-of-the-art methods to implement a more comprehensive detection and recognition system for traffic signs.
- Fine-Grained Predicates Learning for Scene Graph Generation57
This work proposes a method named Fine-Grained Predicates Learning (FGPL) which aims at differentiating among hard-to-distinguish predicates for Scene Graph Generation task, and introduces a Predicate Lattice that helps SGG models to figure out fine-grained predicate pairs.
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- Learning Cross-Aligned Latent Embeddings for Zero-Shot Cross-Modal Retrieval54
This paper proposes a novel method termed Learning Cross-Aligned Latent Embeddings (LCALE) as an alternative to these GAN based methods for ZS-CMR, which seeks for a shared low-dimensional latent space of input multimodal features and class-embeddings by modality-specific variational autoencoders.
- Deep appearance and motion learning for egocentric activity recognition52
Experimental results show that the proposed deep appearance and motion learning (DAML) achieves high performance on both short- and long-term egocentric activity recognition tasks, which is comparable to or even better than the state-of-the-art counterparts.
- Lightweight dynamic conditional GAN with pyramid attention for text-to-image synthesis51
LD-CGAN is a compact and structured single-stream network that consists of one generator and two independent discriminators to regularize and generate 642 and 1282 images in one feed-forward process and significantly decreases the number of parameters and computation time.
- Action-Centric Relation Transformer Network for Video Question Answering50
An action-centric relation transformer network (ACRTransformer) for VideoQA is proposed and a visual feature encoding technique, action-based encoding (ABE), is presented to emphasize the frames with high actionness probabilities (the probability that the frame has actions).
- Deliberate Attention Networks for Image Captioning50
This paper presents a novel Deliberate Residual Attention Network, namely DA, for image captioning, which is equipped with discriminative loss and reinforcement learning to disambiguate image/caption pairs and reduce exposure bias.
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- Residual attention-based LSTM for video captioning48
This paper proposes a novel attention-based framework, namely Residual Attention-based LSTM (Res-ATT), which not only takes advantage of existing attention mechanism but also considers the importance of sentence internal information which usually gets lost in the transmission process.
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- Perceptual Pyramid Adversarial Networks for Text-to-Image Synthesis46
This paper proposes Perceptual Pyramid Adversarial Network (PPAN) to directly synthesize multi-scale images conditioned on texts in an adversarial way and sets new records for text-to-image synthesis on two benchmark datasets.
- A Web-based semantic tagging and activity recognition system for species' accelerometry data46
The evaluation results indicate that the SAAR system enables ecologists with little knowledge of machine learning techniques to collaboratively build classification models with high levels of accuracy, sensitivity, precision and specificity.
- From Global to Local: Multi-Scale Out-of-Distribution Detection45
This work finds that existing models pretrained by off-the-shelf cross-entropy or contrastive losses are incompetent to capture valuable local representations for MODE, due to the scale-discrepancy between the ID training and OOD detection processes, and proposes Attention-based Local PropAgation, a trainable objective that exploits a cross-attention mechanism to align and highlight the local regions of the target objects for pairwise examples.
- Exploiting Scene Graphs for Human-Object Interaction Detection45
Empirical evaluation shows that the proposed SG2HOI method outperforms the state-of-the-art methods on two benchmark HOI datasets: V-COCO and HICO-DET.
- Supervised Hashing with Pseudo Labels for Scalable Multimedia Retrieval45
A Supervised Hashing with Pseudo Labels (SHPL) which uses the cluster centers of the training data to generate pseudo labels, based on which the hash codes can be generated using the criteria of supervised hashing, and it is proved that the pseudo labels and the hash code can be jointly learned and iteratively updated in an unified framework.
- Reliable Few-Shot Learning Under Dual Noises44
DETA++ uses a Contrastive Relevance Aggregation module to calculate image and region weights for support samples, and utilizes an Intra-class Region Swapping strategy to rectify ID class prototypes during task adaptation, enhancing the model’s robustness to the dual noises.
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- Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation43
This paper tackles the issue of scene-object interaction aiming at learning specific knowledge from a scene via an additive attention mechanism and long-tail knowledge transfer which tries to transfer the rich knowledge learned from the head into the tail.
- Foreground-Background Parallel Compression With Residual Encoding for Surveillance Video42
A novel video compression scheme that compresses the foreground and background of the surveillance video separately and the compression ratio is greatly improved by sharing background information among adjacent frames through an adaptive background updating and interpolation module.
- Scalable Multimedia Retrieval by Deep Learning Hashing with Relative Similarity Learning42
A semi-supervised deep learning hashing (DLH) method for fast multimedia retrieval that utilizes both visual and label information to learn an relative similarity graph that can more precisely reflect the relationship among training data, and then generates the hash codes based on the graph.
- Progressive Tree-Structured Prototype Network for End-to-End Image Captioning41
A novel Progressive Tree-Structured prototype Network (dubbed PTSN) is proposed, which is the first attempt to narrow down the scope of prediction words with appropriate semantics by modeling the hierarchical textual semantics.
- Conceptual and Syntactical Cross-modal Alignment with Cross-level Consistency for Image-Text Matching41
This work proposes a Conceptual and Syntactical Cross-modal Alignment with Cross-level Consistency (CSCC) for Image-text Matching by simultaneously exploring the multiple-level cross- modal alignments across the concept and syntactic with a consistency constraint.
- Graph-without-cut: An Ideal Graph Learning for Image Segmentation41
This paper proposes a novel framework, Graph-Without-Cut (GWC), for learning the similarity graph and image segmentations simultaneously, and achieves state-of-the-art performance compared with supervised and unsupervised image segmentation approaches.
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- Beyond Product Quantization: Deep Progressive Quantization for Image Retrieval39
A deep progressive quantization (DPQ) model, as an alternative to PQ, for large scale image retrieval, that is trained once for different code lengths and therefore requires less computation time.
- Synchronization-based clustering on evolving data stream39
This paper proposes a new synchronization-based clustering approach for evolving data streams, called SyncTree, which maintains all micro-clusters at different levels of granularity depending upon the data recency, and provides a principled way to analyze the cluster evolution.
- Dynamic Scene Graph Generation via Temporal Prior Inference38
To efficiently guide the generating of temporal-consistent dynamic scene graphs, the temporal prior inference is incorporated into a DSGG framework by introducing frame enhancement, continuity loss, and fast inference.
- Feature Space Targeted Attacks by Statistic Alignment38
Two novel approaches are designed called Pair-wise Alignment Attack and Global-wiseAlignment Attack, which attempt to measure similarities between feature maps by high-order statistics with translation invariance, and systematically analyze the layer-wise transferability with varied difficulties to obtain highly reliable attacks.
- Dual-Branch Hybrid Learning Network for Unbiased Scene Graph Generation37
A Dual-branch Hybrid Learning network (DHL) to take care of both head predicates and tail ones for SGG, including a Coarse-grained Learning Branch (CLB) and a Fine-grained Learning Branch (FLB).
- Free-Lunch for Cross-Domain Few-Shot Learning: Style-Aware Episodic Training with Robust Contrastive Learning37
Style-aware Episodic Training with Robust Contrastive Learning (SET-RCL) is proposed, which focuses on manipulating the styl distribution of training tasks in the source domain, such that the learned model can achieve better adaption on test tasks with domain-specific styles.
- PoseGTAC: Graph Transformer Encoder-Decoder with Atrous Convolution for 3D Human Pose Estimation36
This work proposes a novel Graph Transformer Encoder-Decoder with Atrous Convolution, named PoseGTAC, to effectively extract multi-scale context and long-range information in the 3D human pose estimation task.
- Skeleton-based Action Recognition via Adaptive Cross-Form Learning34
Adaptive Cross-Form Learning (ACFL) is presented, which empowers well-designed GCNs to generate complementary representation from single-form skeletons without changing model capacity, and significantly improves various GCN models, achieving a new record for skeleton-based action recognition.
- Label-Attended Hashing for Multi-Label Image Retrieval34
A Label-Attended Hashing (LAH) algorithm is proposed which enables an end-to-end hash model with inter-dependency feature extraction and the usage of co-occurrence relationship and MFB not only promotes the precision of hash codes but also accelerates the hash learning.
- DETA: Denoised Task Adaptation for Few-Shot Learning33
DEnoised Task Adaptation (DETA) is proposed, a first, unified image- and label-denoising framework orthogonal to existing task adaptation approaches that filters out task-irrelevant, noisy representations by taking advantage of both global visual information and local region details of support samples.
- Question-Led object attention for visual question answering33
This work proposes a novel but simple framework, namely Question-Led Object Attention (QLOB), to improve the VQA performance by exploring question semantics, fine-grained object information, and the relationship between those two modalities.
- Examine before You Answer33
This work proposes a novel approach, namely Multi-task Learning with Adaptive-attention (MTA), to simulate human logics for MC VQA, which first fuse the answer options and question features, and then adaptively attend to the visual features for inferring a MC V QA.
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- Visual Commonsense-Aware Representation Network for Video Captioning31
A simple, yet effective method, called visual commonsense-aware representation network (VCRN), for video captioning that is integrated into the existing method of video question answering (VideoQA) and improves this performance, which further demonstrates the generalization capability of the method.
- One-shot Scene Graph Generation31
This paper designs a task named One-Shot Scene Graph Generation, where each relationship triplet comes from only one labeled example, and proposes Multiple Structured Knowledge (Relational Knowledge and Commonsense Knowledge) for the one-shot scene graph generation task.
- MPT: Multi-grained Prompt Tuning for Text-Video Retrieval30
A multi-grained video encoder that employs spatial, temporal, and global prompts to transfer the base-generic knowledge from the image-text pre-trained model while comprehensively excavating determinative video-specific characteristics is devised.
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- Class Gradient Projection For Continual Learning30
Class Gradient Projection is proposed, which calculates the gradient subspace from individual classes rather than tasks, and a Base Refining algorithm is designed to combine similar classes and refine class bases dynamically to improve the model's ability to handle unseen tasks.
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- Deep Region Hashing for Generic Instance Search from Images30
An effective and efficient Deep Region Hashing (DRH) approach for large-scale INS using an image patch as the query and can achieve even better performance than the state of thearts in terms of mAP, while the efficiency is improved by nearly 100 times.
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- Curriculum-Based Meta-learning29
This paper presents a Curriculum-Based Meta-learning (CubMeta) method to train the meta-learner using tasks from easy to hard, and in each step, a module named BrotherNet is designed to establish harder tasks and an effective learning scheme for obtaining an ensemble of stronger meta-learners.
- Low-rank network signatures in the triple network separate schizophrenia and major depressive disorder29
Experiments indicate that SCNMF allows extracting the low-rank discriminative patterns between the two disorders, achieving a classification accuracy of 82.6% based on the extracted functional and structural abnormalities with support vector machine.
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- Cognitive visual anomaly detection with constrained latent representations for industrial inspection robot28
A novel cognitive VAD method for industrial inspection robot is proposed based on a convolutional generative adversarial network and a denoising auto-encoder, where the adversarial learning mechanism is adopted to establish the boundary between the normal and anomaly data.
- Sharp and Real Image Super-Resolution Using Generative Adversarial Network28
A novel residual network architecture based on generative adversarial network (GAN) for image super-resolution (SR), which is capable of inferring photo-realistic images for 4\(\times \) upscaling factors, and demonstrates that the proposed approach performs better than previous methods.
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- Correlated Features Synthesis and Alignment for Zero-shot Cross-modal Retrieval27
This work proposes a novel Correlated Feature Synthesis and Alignment (CFSA) approach to integrate multimodal feature synthesis, common space learning and knowledge transfer for zero-shot cross-modal retrieval (ZS-CMR), and demonstrates the remarkable improvements achieved by this method comparing with a bundle of state-of-the-art approaches.
- Spatial and temporal scoring for egocentric video summarization27
This work utilizes egocentric salience cues, motion cues and a selection model to capture stable salience weight, discriminative weight and representative weight of a video shot respectively and combines these weights in a unified framework to predict the importance score of a shot.
- InSpire: Vision-Language-Action Models with Intrinsic Spatial Reasoning26
InSpire redirects the VLA’s attention to task-relevant factors by prepending the question "In which direction is the [object] relative to the robot?" to the language instruction and aligning the model’s output answer "right/left/up/down/front/back/grasped" and predicted actions with ground-truth.
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- Self-representation nearest neighbor search for classification26
This paper first designs a self-reconstruction method to reconstruct each data point by all the data, and the derived reconstruction coefficient is then used for calculating the k value for each training sample.
- Deep and fast: Deep learning hashing with semi-supervised graph construction26
A semi-supervised deep learning hashing (DLH) method for fast multimedia retrieval that utilizes both visual and label information to learn an optimal similarity graph that can more precisely encode the relationship among training data and then generate the hash codes based on the graph.
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- Toward a Unified Transformer-Based Framework for Scene Graph Generation and Human-Object Interaction Detection24
This work introduces SG2HOI+, a unified one-step model based on the Transformer architecture that employs two interactive hierarchical Transformers to seamlessly unify the tasks of SGG and HOI detection and achieves competitive performance when compared to state-of-the-art HOI methods.
- Adaptive Fine-Grained Predicates Learning for Scene Graph Generation23
An Adaptive Fine-Grained Predicates Learning (FGPL-A) which aims at differentiating hard-to-distinguish predicates for SGG is proposed, which progressively regularize model's discriminating process with fine-grained supervision concerning model's dynamic learning status, ensuring balanced and efficient learning process.
- Real-time social media retrieval with spatial, temporal and social constraints23
This paper proposes an interval-at-a-time (IAAT) framework as a first attempt to provide a one-size-fits-all solution to social media retrieval with spatial, temporal and social constraints and shows that it performs better than most of the state-of-the-art customized solutions in a variety of query types.
- Joint Graph Learning and Video Segmentation via Multiple Cues and Topology Calibration23
A novel framework, joint graph learning and video segmentation (JGLVS) is proposed, which learns the similarity graph and video segmentsation simultaneously simultaneously and achieves promising performance on the benchmark dataset which outperforms the state-of-the-art by up to 11% for the BPR metric.
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- Deep Recurrent Quantization for Generating Sequential Binary Codes21
This work proposes a Deep Recurrent Quantization (DRQ) architecture which can generate sequential binary codes and achieves comparable or even better performance compared with the state-of-the-art for image retrieval.
- Exploiting long-term temporal dynamics for video captioning21
Experimental results obtained in two public video captioning benchmarks indicate that the novel temporal and spatial LSTM (TS-LSTM) outperforms the state-of-the-art methods.
- Large-scale image retrieval with supervised sparse hashing21
This paper reduces the effective number of parameters involved in the learned projection matrix according to sparsity regularizer, which helps avoid overfitting problem and effectively lower both the storage and computation cost.
- Depth-Aware Sparse Transformer for Video-Language Learning20
To guarantee computational efficiency, a depth- aware sparse attention modular with linear computational complexity is designed for each transformer layer to learn depth-aware 2D representations, and a hierarchical structure to maintain multi-scale temporal coherence across long-range dependencies is designed.
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- Play and rewind: Context-aware video temporal action proposals20
This work proposes a context-aware temporal action proposal network which makes full use of the contextual information in two aspects and designs an action-attention based re-ranking network which considers both surrounding proposal and initial actionness scores to assign true action proposals with high confidence scores.
- Deep Discrete Hashing with Self-supervised Pairwise Labels20
Experimental results on standard datasets demonstrate that the proposed novel unsupervised deep hashing method, named Deep Discrete Hashing (DDH), significantly outperforms existing hashing methods by large margin in terms of mAP for image retrieval and object recognition.
- A Closer Look at Conditional Prompt Tuning for Vision-Language Models19
This work identifies a critical issue with existing conditional PT methods, and proposes Class-adaptive Prompt Tuning (CaPT), which enables fast adaptation of tuned models to new classes by learning TCI-conditioned prompts from base classes, and can be used as a plugin to mitigate the BNT problem for existing unconditional PT schemes.
- Bottom-up and Top-down: Bidirectional Additive Net for Edge Detection19
Experimental results show that the proposed methods can improve the edge detection performance to new records and achieve state-of-the-art results on two public benchmarks: BSDS500 and NYUDv2.
- One Network for Multi-Domains: Domain Adaptive Hashing with Intersectant Generative Adversarial Networks19
An end-to-end domain adaptive learning framework that simultaneously and precisely generates discriminative hash codes and classifies target domain images that is superior to the other state-of-the-art methods on the tasks of object recognition and image retrieval.
- Understanding and improving ontology reasoning efficiency through learning and ranking19
A comprehensive evaluation on a large and carefully curated ontology corpus shows that R 2 O 2 * outperforms all six component reasoners as well as AutoFolio, a robust and strong algorithm selection system.
- Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves18
Skip Tuning is proposed, a novel paradigm for adapting VLMs to downstream tasks that applies Layer-wise Skipping and Classwise Skipping upon the FT baseline without introducing extra context vectors or adapter modules.
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- SNEQ: Semi-Supervised Attributed Network Embedding with Attention-Based Quantisation18
Evaluation on four real-world networks of diverse characteristics shows that SNEQ outperforms a number of state-of-the-art embedding methods in link prediction, node classification and node recommendation and the quantised embedding shows a great advantage in terms of storage and time compared with continuous embeddings as well as hashing methods.
- Practical No-Box Adversarial Attacks With Training-Free Hybrid Image Transformation17
The existence of a training-free adversarial perturbation under the no-box threat model, which can be successfully used to attack different DNNs in real-time is shown and is even competitive to mainstream transfer-based black-box attacks.
- Adaptive Multi-Path Aggregation for Human DensePose Estimation in the Wild17
The experimental results on the challenging Dense-COCO dataset demonstrate that the proposed AMA-net approach sets a new record for Dense Human Pose Estimation task, and it significantly outperforms the state-of-the-art methods.
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- Fully Functional Image Manipulation Using Scene Graphs in A Bounding-Box Free Way16
This method is designed to be the first to support fully functional image manipulation using scene graphs, including adding, removing, replacing and repositing instances, and significantly surpasses the state-of-the-art both quantitatively and qualitatively.
- Relation Regularized Scene Graph Generation16
A relation regularized network (R2-Net) is proposed, which can predict whether there is a relationship between two objects and encode this relation into object feature refinement and better SGG.
- From Channel Bias to Feature Redundancy: Uncovering the “Less is More” Principle in Few-Shot Learning15
A simple yet effective soft-masking method, Augmented Feature Importance Adjustment (AFIA), which estimates feature importance from augmented data to mitigate the issue of channel bias and provides a foundational principle for few-shot representation transfer and a practical method for developing more robust few-shot learning algorithms.
- F³-Pruning: A Training-Free and Generalized Pruning Strategy towards Faster and Finer Text-to-Video Synthesis15
This work proposes a training-free and generalized pruning strategy called F3-Pruning to prune redundant temporal attention weights of two mainstream T2V models using transformers and diffusion models.
- KTN: Knowledge Transfer Network for Learning Multiperson 2D-3D Correspondences15
This work presents a novel framework to detect the densepose of multiple people in an image by using a refinement of pyramidal representation, where it simultaneously maintains feature resolution and suppresses background pixels, and this strategy results in a substantial increase in accuracy.
- RSGNet: Relation based Skeleton Graph Network for Crowded Scenes Pose Estimation15
A Skeleton Graph Machine (SGM) is introduced to model the skeleton-based commonsense knowledge, aiming to estimate the target pose with the constraint of human body structure, which can help to deal with the challenges in crowded scenes from a reasoning perspective.
- Semisupervised Network Embedding With Differentiable Deep Quantization15
d-SNEQ is developed, a differentiable DNN-based quantization method for network embedding that incorporates a rank loss to equip the learned quantization codes with rich high-order information and is able to substantially compress the size of trained embeddings, thus reducing storage footprint and accelerating retrieval speed.
- Feature aggregating hashing for image copy detection15
A fast feature aggregating method for image copy detection which uses machine learning based hashing to achieve fast feature aggregation and generates binary codes which leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases.
- Attention Hijackers: Detect and Disentangle Attention Hijacking in LVLMs for Hallucination Mitigation14
A novel, training-free strategy namely Attention HIjackers Detection and Disentanglement (AID), designed to isolate the influence of Hijackers, enabling LVLMs to rely on their context-aware intrinsic attention map, to reduce hallucination.
- GT23D-Bench: A Comprehensive General Text-to-3D Generation Benchmark14
GT23D-Bench is introduced, the first comprehensive benchmark specifically designed for GT23D training and evaluation, and a comprehensive evaluation suite with 10 metrics assessing both text-3D alignment and 3D visual quality at multiple levels, demonstrating significantly higher correlation with human judgment compared to existing methods.
- Learning to Generate Scene Graph from Head to Tail14
A novel SGG framework, learning to generate scene graphs from Head to Tail (SGG-HT), containing Curriculum Re-weight Mechanism (CRM) and Semantic Context Module (SCM) is proposed to relieve semantic deviation by ensuring the semantic consistency between the generated scene graph and the ground truth in global and local representations.
- Semantic-aware Transfer with Instance-adaptive Parsing for Crowded Scenes Pose Estimation14
This work introduces a novel method named Semantic-aware Transfer with Instance-adaptive Parsing (STIP), which enhances the discriminative power of pixel-level representations with a semantic-aware mechanism, where it smartly decides which pixels to enhance and what semantic embeddings to add.
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- Kernel based latent semantic sparse hashing for large-scale retrieval from heterogeneous data sources14
KLSSH firstly employs sparse coding for obtaining primary latent features of image and matrix factorization for generating features of text concepts to learn latent semantic features in a high level abstraction space and maps the latent semantic feature to compact binary codes using kernel method.
- Zero-shot Image Categorization by Image Correlation Exploration14
Experimental results suggest that semi-supervised learning do enhance the performances of attribute prediction and zero-shot categorization, compared with state-of-the-art methods.
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- KTN: Knowledge Transfer Network for Multi-person DensePose Estimation13
This paper addresses two main problems: how to design a simple yet effective pipeline for densepose estimation; and how to equip this pipeline with the ability of handling the issues of limited annotations and class-imbalanced labels.
- Fusion by synthesizing: A multi-view deep neural network for zero-shot recognition13
A novel multi-view deep neural network, termed Fusion by Synthesis (FS), which leverages word embeddings of classes as complementary for attributes and performs zero-shot prediction by fusing the word embedDings of unseen classes and the synthesized attributes in the visual feature space.
- Policy Contrastive Decoding for Robotic Foundation Models12
This work proposes a novel Policy Contrastive Decoding (PCD) approach, which redirects the robot policy's focus toward object-relevant visual clues by contrasting action probability distributions derived from original and object-masked visual inputs.
- KE-RCNN: Unifying Knowledge-Based Reasoning Into Part-Level Attribute Parsing12
This article proposes a knowledge-embedded RCNN (KE-RCNN) to identify attributes by leveraging rich knowledge, including implicit knowledge and explicit knowledge, to enhance part-level representation by encoding part–part relational contexts into part boxes.
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- Camera-Agnostic Person Re-Identification via Adversarial Disentangling Learning12
An Adversarial Disentangling Learning (ADL) framework to decouple camera-related and ID-related features, which can be readily used for camera-agnostic person ReID, and designs an effective solution to approximate and minimize mutual information by transforming it into a discrimination problem.
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- Unsupervised urban scene segmentation via domain adaptation12
An adversarial learning method for domain adaptation from the perspectives of both image-level and feature-level that adapts images from two domains to appear as if drawn from one domain and the latter attempts to learn similarities between the source and target domains in feature- level common space.
- Lab2Pix: Label-Adaptive Generative Adversarial Network for Unsupervised Image Synthesis12
This work proposes an unsupervised framework named Lab2Pix to adaptively synthesize images from labels by elegantly considering the particular properties of label to image synthesis task, and designs the generator in a cumulative style which gradually renders synthesized images by fusing features in different levels.
- Text-Video Retrieval With Global-LocalSemantic Consistent Learning11
This work introduces a parameter-free global interaction module to explore coarse-grained alignment and devise a shared local interaction module that employs several learnable queries to capture latent semantic concepts for learning fine-grained alignment in text-video retrieval.
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- Processing Long Queries Against Short Text11
This work proposes a novel rank-aware block-oriented inverted index scheme, and introduces a block-at-a-time search strategy based on the index scheme to support a much tighter upper bound estimation and a very early termination.
- Estimating Fire Weather Indices Via Semantic Reasoning Over Wireless Sensor Network Data Streams11
The underlying ontologies, the semantic reasoning and the Semantic Fire Weather Index (SFWI) system that is developed to enable domain experts to specify and adapt rules for calculating Fire Weather Indices are described and the Web-based mapping interface is described that enables users to improve their understanding of how fire weather indices vary over time within a particular region.
- Informative Scene Graph Generation via Debiasing10
DB-SGG is proposed, an effective framework based on debiasing but not the conventional distribution fitting that can be easily applied to SGG models and outperforms Transformer by 136.3% on mR@20 at three SGG sub-tasks on the SGG-VG dataset.
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- Precise Target-Oriented Attack against Deep Hashing-based Retrieval9
This work proposes a novel Precise Target-Oriented Attack dubbed PTA, to enhance the precision of targeted attacks with stronger manipulability, and further categorizes the general target label into preciser single target label for attack.
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- Make-A-Storyboard: A General Framework for Storyboard with Disentangled and Merged Control9
A general framework coined as Make-A-Storyboard that applies disentangled control over the consistency of contextual correlated characters and scenes and then merge them to form harmonized images is designed, which could be seamlessly integrated into mainstream Image Customization methods, empowering them with the capability of story visualization.
- MotionZero:Exploiting Motion Priors for Zero-shot Text-to-Video Generation9
A prompt-adaptive and disentangled motion control strategy coined as MotionZero, which derives motion priors from prompts of different objects by Large-Language-Models and accordingly applies motion control of differentObjects to corresponding regions in disentanglement is proposed.
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- Temporal Denoising Mask Synthesis Network for Learning Blind Video Temporal Consistency9
A novel model termed Temporal Denoising Mask Synthesis Network (TDMS-Net) is proposed that jointly predicts the motion mask, soft optical flow and the refining mask to synthesize the temporal consistent frames to enforce temporal consistency in a video.
- Exploiting score distribution for heterogenous feature fusion in image classification9
The effect of exploiting score distribution to address the feature fusion problem and proposing a novel method named score-distribution MKL (SD-MKL) for image classification, which significantly benefits the performance of image classification.
- Event Video Mashup: From Hundreds of Videos to Minutes of Skeleton9
A novel approach to automatically generate a unified short video from a collection of Web videos to describe the storyline of an event, using a submodular based content selection model that embodies both importance and diversity to depict the event from comprehensive aspects in an efficient way.
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- MagicVFX: Visual Effects Synthesis in Just Minutes8
This work sets up two paradigms according to the need for pre-produced effects or not, and explores the capabilities of a pre-trained text-to-video model to synthesize visual effects within these two paradigms, evidencing the significant potential of existing AIGC technology for application in visual effects synthesis tasks.
- CUCL: Codebook for Unsupervised Continual Learning8
A method named Codebook for Unsupervised Continual Learning (CUCL) is proposed which promotes the model to learn discriminative features to complete the class boundary and significantly boosts the performances of supervised and unsupervised methods.
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- Cumulative Nets for Edge Detection8
A Cumulative Network (C-Net), which learns the side network cumulatively based on current visual features and low-level side outputs, to gradually remove detailed or sharp boundaries to enable high-resolution and accurate edge detection.
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- SI-BiViT: Binarizing Vision Transformers with Spatial Interaction7
A ViT binarization approach dubbed SI-BiViT is designed to incorporate spatial interaction in the binarization process and enhances the classification performance of binarized ViTs by an average of 10.52% in Top-1 accuracy compared to the previous state-of-the-art.
- 7
- 7
- MobileVidFactory: Automatic Diffusion-Based Social Media Video Generation for Mobile Devices from Text7
A system to automatically generate vertical mobile videos where users only need to give simple texts mainly, and allows users to add specified screen texts for enriching visual expression, and specify texts for automatic reading with optional voices as they like.
- Continual Referring Expression Comprehension via Dual Modular Memorization7
This paper proposes an effective baseline method named Dual Modular Memorization (DMM), which alleviates the problem of catastrophic forgetting by two memorization modules: Implicit-Memory and Explicit-Memory, and creates three benchmarks for the new CREC setting.
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- Coarse-to-fine Image Co-segmentation with Intra and Inter Rank Constraints7
A novel coarse-to-fine co-segmentation (CFC) framework is proposed, which utilizes the coarse foreground and background proposals to learn a robust similarity measure of the features in an unsupervised way, and then devises a simple objective function based on the definition of image co- segmentation.
- Training-Free Semantic Video Composition via Pre-trained Diffusion Model6
A training-free pipeline employing a pre-trained diffusion model imbued with semantic prior knowledge is proposed, which can process composite videos with broader semantic disparities, demonstrating efficacy in managing broader semantic disparities.
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- Support-Set Based Multi-Modal Representation Enhancement for Video Captioning6
A novel and flexible framework, namely Support-set based Multi-modal Representation Enhancement (SMRE) model, to mine rich information in a semantic subspace shared between samples to learn underlying connections between samples and obtain semantic-related visual elements.
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- Multiple hierarchical deep hashing for large scale image retrieval6
This paper proposes a Multiple Hierarchical Deep Hashing (MHDH) approach for large scale image retrieval that seeks to integrate multiple hierarchical non-linear transformations with hidden neural network layer for hashing code generation.
- Corrigendum to “A web-based semantic tagging and activity recognition system for species' accelerometry data” [Ecol. Inf. 13 (2013) 47–56]6
The original paper stated that ‘All procedures were carried out with the approval of The University of Queensland Animal Ethics Committee (Approval no: SBS/300/12/NECTAR)’ but at the time of publication approval for the low risk human research presented here had not been obtained.
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- Generalized Unbiased Scene Graph Generation5
The Multi-Concept Learning (MCL) framework is proposed, which ensures a balanced learning process across rare/ uncommon/ common concepts and introduces the Balanced Prototypical Memory (BPM), which guides SGG models to generate balanced representations for concept-prototypes.
- 5
- 5
- 3D Self-Attention for Unsupervised Video Quantization5
This paper makes a first attempt to combine quantization method with video retrieval called 3D-UVQ, which obtains high retrieval accuracy with low storage cost and demonstrates that the method significantly outperforms the state-of-the-arts.
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- RepParser: End-to-End Multiple Human Parsing with Representative Parts4
RepParser solves the multiple human parsing in a new single-stage manner without resorting to person detection or post-grouping, by empowering the parsing pipeline by representative parts, since they are characterized by instance-aware keypoints and can be utilized to dynamically parse each person instance.
- Towards Unsupervised Deformable-Instances Image-to-Image Translation4
An effective pipeline named Mask-Guided Deformable-instances GAN (MGD-GAN) which first generates target masks in batch and then utilizes them to synthesize corresponding instances on the background image, with all instances efficiently translated and background well preserved is proposed.
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- Deep Hash-based Relevance-aware Data Quality Assessment for Image Dark Data4
A Deep Hash-based Relevance-aware Data Quality Assessment framework, which contains off-line learning and relevance mining parts as well as an on-line assessing part, which can gain the most desired data with the same mining resources.
- A framework for image dark data assessment4
The results on real-world dataset show the proposed assessment framework can apply to large-scale datasets, help users evaluate the dark data by different requirements, and assist the user to conduct subsequent data mining work.
- Movie Fill in the Blank with Adaptive Temporal Attention and Description Update4
This paper proposes to use a novel LSTM network called L STM with Linguistic gate (LSTMwL), which exploits adaptive temporal attention for MovieFIB, and demonstrates that this approach outperforms state-of-the-art models for movieFIB.
- Jointly Learning Attentions with Semantic Cross-Modal Correlation for Visual Question Answering4
A novel end-to-end model to jointly learn attentions with semantic cross-modal correlation for efficiently solving the VQA problem is proposed and Experimental results on the benchmark datasets demonstrate that the model outperforms several state-of-the-art techniques for V QA.
- Exploring Viewable Angle Information in Georeferenced Video Search4
This study proposes a novel circular sector model with beginning-ending vectors for FOV representation which additionally explores viewable angle information and demonstrates how the model can be applied to perform different types of overlap queries for spatial data selection in a unified framework, while providing competitive performance in terms of efficiency.
- SeMv-3D: Toward Concurrency of Semantic and Multi-View Consistency in General Text-to-3D Generation3
This work proposes SeMv-3D, a novel framework that jointly enhances semantic alignment and multi-view consistency in GT23D generation and introduces Triplane Prior Learning (TPL), which effectively learns triplane priors by capturing spatial correspondences across three orthogonal planes using a dedicated Orthogonal Attention mechanism.
- Sim-and-Human Co-training for Data-Efficient and Scene-Generalizable Bimanual Manipulation3
SimHum is presented, a co-training framework to simultaneously extract kinematic prior from simulated robot actions and visual prior from real-world human observations, which achieves data-efficient and generalizable robotic manipulation in real-world tasks.
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- Boosting Adversarial Attacks by Leveraging Decision Boundary Information3
A Boundary Fitting Attack to improve transferability is proposed, introducing a method to obtain a set of boundary points and leverage the gradient information of these points to update the adversarial examples and can be combined with existing gradient-based methods.
- 3
- Publishing, Linking and Annotating Events via Interactive Timelines: an Earth Sciences Case Study3
This paper describes the interoperable integration, annotation and linking of information about major events from the earth sciences domain, by adopting a Linked Data approach to major events (earthquakes, tsunamis and volcanic eruptions) and the timelines and annotations that capture additional domain-expert knowledge.
- Janus-LoRA: A Balanced Low-Rank Adaptation for Continual Learning2
Comprehensive experiments on challenging benchmarks demonstrate that by harmonizing parameter-level orthogonality with feature-level separation, Janus-LoRA achieves a superior balance and establishes new state-of-the-art performance.
- 2
- EvoGAN2
An Evolutionary GANs (EvoGAN) for face aging/rejuvenation is proposed by making each age transformation smooth and decomposing a long-term transformation into several short-terms, which can better predict age-specific factors.
- Beyond the Majority: Long-tail Imitation Learning for Robotic Manipulation1
APA is introduced, a simple yet effective scheme that transfers knowledge from data-rich head tasks to data-scarce tail tasks without requiring external demonstrations and reveals that data scarcity on tail tasks directly impairs the policy’s spatial reasoning capability.
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- AICL: Action In-Context Learning for Text-to-Video Generation1
This work proposes Acton In-Context Learning (AICL), a novel approach to generate intricate actions by emulating motions from pre-existing videos in the inference stage using a plug-and-play method, and significantly improves the generation of realistic actions, even in situations where existing VDMs might directly fail.
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- Towards Generalized and Training-Free Text-Guided Semantic Manipulation1
This work proposes a novel $\textit{GTF}$ for text-guided semantic manipulation, which supports multiple semantic manipulations and produces high-fidelity results via simply controlling the geometric relationship between noises without tuning or optimization.
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- 1
- CIParsing: Unifying Causality Properties into Multiple Human Parsing1
This work presents a causality inspired parsing paradigm termed CIParsing, which assumes that an input image is constructed by a mix of causal factors and non-causal factors, where only the former ones cause the generation process of human parsing.
- Local-Global Information Interaction Debiasing for Dynamic Scene Graph Generation1
A novel DynSGG model based on multi-task learning, DynS GG-MTL, is proposed, which introduces the local interaction information and global human-action interaction information, which improves the dynamic scene graph generation but also alleviates the long-tail problem.
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- Generalized Image Retrieval with Off-The-Shelf Quantizer–
A cross-aligned contrastive learning objective for model training that simultaneously reduces quantization error and domain gap, encouraging the model to generate domain-invariant quantization codes and improves generalization ability on unknowns is proposed.
- TIMI: Training-Free Image-to-3D Multi-Instance Generation with Spatial Fidelity–
TIMI is proposed, a novel Training-free framework for Image-to-3D Multi-Instance generation that achieves high spatial fidelity and yields better performance in terms of both global layout and distinct local instances compared to existing multi-instance methods, without requiring additional training and with faster inference speed.
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- Dynamic Pattern Alignment Learning for Pretraining Lightweight Human-Centric Vision Models–
DPAL-ViT/Ti (5M parameters) achieves surprising generalizability similar to existing large HVMs such as PATH-B and Sapiens-L and outperforms previous distillation-based pretraining methods including Proteus-ViT/Ti (5M) and TinyMiM-ViT/Ti (5M) by a large margin.
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- Learning Generalizable and Efficient Image Watermarking via Hierarchical Two-Stage Optimization–
A Hierarchical Watermark Learning (HiWL) framework is proposed, a two-stage optimization that enables a watermarking model to simultaneously achieve all three criteria for generalizable watermarking and achieves 7.6% higher accuracy in watermark extraction compared to existing methods while maintaining extremely low latency.
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- Reversible Inversion for Training-Free Exemplar-guided Image Editing–
The ReInversion method is introduced, which operates as a two-stage denoising process, which is first conditioned on the source image and subsequently on the reference, and which achieves state-of-the-art EIE performance with the lowest computational overhead.
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- SeMv-3D: Towards Concurrency of Semantic and Multi-view Consistency in General Text-to-3D Generation–
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- ALF: Adaptive Label Finetuning for Scene Graph Generation–
A plug-and-play method named CITrans, which iteratively trains SGG models with progressively enhanced data and introduces Context-Restricted Transfer (CRT), which imposes subject-object constraints within predicates’ semantic space to achieve fine-grained data transfer.
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- Adaptive Visual Memory Network for Visual Dialog–
An adaptive visual memory network (AVMN), which applies external memory bank to directly store grounded visual information to solve the key challenge in this visual dialog is visual co-reference resolution.
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- A System for Interactive and Intelligent AD Auxiliary Screening–
An Interactive and Intelligent AD Auxiliary Screening (IAS) system consisting of speech-based Interactive Unit Testing Module (IUTM) and truth-based Intelligent Analysis module (IAM), both of which are developed by deep learning techniques.
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- Highly Accurate Distributed Classification of Web Documents–
Results from the experiments show that the approach could gain higher classification accuracy over traditional centralized text classifiers, and require less memory and computational time.
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- Streamlining the analysis of accelerometry data to improve understanding of animal behaviour–
The Semantic Annotation and Activity Recognition (SAAR) system is presented which supports storing, visualizing, annotating and automatic recognition of tri-axial accelerometer data streams by integrating semantic annotation and visualization services with Support Vector Machine (SVM) techniques.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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